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Title: GitHub - junjun315/models: Model configurations

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Description: Model configurations. Contribute to junjun315/models development by creating an account on GitHub.

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ImageNet Classification with Deep Convolutional Neural Networkshttps://www.researchgate.net/publication/267960550_ImageNet_Classification_with_Deep_Convolutional_Neural_Networks
VGGhttps://github.com/PaddlePaddle/models/tree/develop/fluid/PaddleCV/image_classification/models
Very Deep ConvNets for Large-Scale Inage Recognitionhttps://arxiv.org/pdf/1409.1556.pdf
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Going deeper with convolutionshttps://ieeexplore.ieee.org/document/7298594
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Deep Residual Learning for Image Recognitionhttps://arxiv.org/abs/1512.03385
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Inception-ResNet and the Impact of Residual Connections on Learninghttp://arxiv.org/abs/1602.07261
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MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationshttps://arxiv.org/abs/1704.04861
DPNhttps://patch-diff.githubusercontent.com/junjun315/models/blob/develop/fluid/PaddleCV/image_classification/models
Dual Path Networkshttps://arxiv.org/abs/1707.01629
SE-ResNeXthttps://patch-diff.githubusercontent.com/junjun315/models/blob/develop/fluid/PaddleCV/image_classification/models
Squeeze-and-excitation networkshttps://arxiv.org/abs/1709.01507
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SSD: Single Shot MultiBox Detectorhttps://arxiv.org/abs/1512.02325
Face Detector: PyramidBoxhttps://patch-diff.githubusercontent.com/junjun315/models/blob/develop/fluid/PaddleCV/face_detection/README_cn.md
PyramidBox: A Context-assisted Single Shot Face Detectorhttps://arxiv.org/pdf/1803.07737.pdf
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkshttps://arxiv.org/abs/1506.01497
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Mask R-CNNhttps://arxiv.org/abs/1703.06870
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ICNet for Real-Time Semantic Segmentation on High-Resolution Imageshttps://arxiv.org/abs/1704.08545
DCGANhttps://patch-diff.githubusercontent.com/junjun315/models/blob/develop/fluid/PaddleCV/gan/c_gan
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkshttps://arxiv.org/pdf/1511.06434.pdf
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Conditional Generative Adversarial Netshttps://arxiv.org/abs/1411.1784
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Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networkshttps://arxiv.org/abs/1703.10593
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Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networkshttps://www.researchgate.net/publication/221346365_Connectionist_temporal_classification_Labelling_unsegmented_sequence_data_with_recurrent_neural_'networks
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Attention Is All You Needhttps://arxiv.org/abs/1706.03762
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understandinghttps://arxiv.org/abs/1810.04805
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Chinese Lexical Analysis with Deep Bi-GRU-CRF Networkhttps://arxiv.org/abs/1807.01882
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Multi-Turn Response Selection for Chatbots with Deep Attention Matching Networkhttp://aclweb.org/anthology/P18-1103
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Session-based Recommendations with Recurrent Neural Networkshttps://arxiv.org/abs/1511.06939
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Multi-Rate Deep Learning for Temporal Recommendationhttps://dl.acm.org/citation.cfm?id=2914726
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DeepFM: A Factorization-Machine based Neural Network for CTR Predictionhttps://arxiv.org/abs/1703.04247
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A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systemshttp://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/frp1159-songA.pdf
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Human-level control through deep reinforcement learninghttps://www.nature.com/articles/nature14236
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Font Size: Deep Reinforcement Learning with Double Q-Learninghttps://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/viewPaper/12389
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Dueling Network Architectures for Deep Reinforcement Learninghttp://proceedings.mlr.press/v48/wangf16.html
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